Abstract
Changing climate has altered the trends and variability of precipitation and temperature globally and thereby increasing the risk of natural and social disasters, especially in coastal climatic transitional zones such as the Huaihe river basin (HRB). This paper applies the Empirical Quantile Mapping (EQM) method for bias correction and systematically evaluates the performance of the 30 Global climate models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating precipitation and temperature over the Huaihe river basin (HRB) for 1979–2014. A suitable multi-model ensemble subset (BMME) is selected based on the Euclidean Distance (ED) synthetic metric. Results show that in comparison to the baseline period 1995–2014, precipitation (temperature) over HRB is projected to increase at the rate of around 15 mm/decade (0.1 °C/decade), 16 mm/decade (0.3 °C/decade), 20 mm/decade (0.5 °C/decade), and 15 mm/decade (0.6 °C/decade) for the period 2015–2100, under Shared Socioeconomic Pathways SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. In the long-term (2081–2100), the annual precipitation is projected to increase by 32%, 27%, 35%, and 26%, under the four scenarios, respectively. The temperature is projected to remain stable or slightly decrease under SSP1-2.6 (2 °C) and SSP2-4.5 (3 °C), while will keep rising and increasing by 6 °C under SSP5-8.5 and by 4 °C under SSP3-7.0 by 2100. The isotherm and isohyet are projected to shift northwest from southeastern coastal China to inland in the future, which is likely associated with the northwestward shift of the western Pacific anticyclone in summer.
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Data availability
The ground meteorological observation data is provided by the National Meteorological Information Center of China at http://data.cma.gov.cn/. The CMFD data is provided by Qinghai-Tibet Plateau Research Institute of the Chinese Academy of Sciences at http://data.tpdc.ac.cn/zh-hans/. The CMIP6 data is available online at https://esgf-node.llnl.gov/search/cmip6/.
References
Ahmad I, Tang D, Wang T, Wang M, Wagan B (2015) Precipitation trends over time using Mann-Kendall and Spearman’s rho tests in Swat River Basin. Pakistan Adv Meteorol 2015:1–15. https://doi.org/10.1155/2015/431860
Ahmed K, Sachindra DA, Shahid S, Demirel MC, Chung E-S (2019) Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrol Earth Syst Sci 23:4803–4824. https://doi.org/10.5194/hess-23-4803-2019
Alexandrov V, Hoogenboom G (2000) The impact of climate variability and change on crop yield in Bulgaria. Agric for Meteorol 104:315–327. https://doi.org/10.1016/S0168-1923(00)00166-0
Almazroui M, Ashfaq M, Islam MN, Rashid IU, Kamil S, Abid MA, O’Brien E, Ismail M, Reboita MS, Sörensson AA, Arias PA, Alves LM, Tippett MK, Saeed S, Haarsma R, Doblas-Reyes FJ, Saeed F, Kucharski F, Nadeem I, Silva-Vidal Y, Rivera JA, Ehsan MA, Martínez-Castro D, Muñoz ÁG, Ali MA, Coppola E, Sylla MB (2021a) Assessment of CMIP6 performance and projected temperature and precipitation changes over South America. Earth Syst Environ 5:155–183. https://doi.org/10.1007/s41748-021-00233-6
Almazroui M, Saeed F, Saeed S, Ismail M, Ehsan MA, Islam MN, Abid MA, O’Brien E, Kamil S, Rashid IU, Nadeem I (2021b) Projected changes in climate extremes using CMIP6 simulations over SREX regions. Earth Syst Environ 5:481–497. https://doi.org/10.1007/s41748-021-00250-5
Almazroui M, Saeed F, Saeed S, Nazrul Islam M, Ismail M, Klutse NAB, Siddiqui MH (2020) Projected Change in temperature and precipitation over Africa from CMIP6. Earth Syst Environ 4:455–475. https://doi.org/10.1007/s41748-020-00161-x
Anderberg M (1973) Cluster analysis for applications. Elsevier. https://doi.org/10.1016/C2013-0-06161-0
Arunrat N, Sereenonchai S, Chaowiwat W, Wang C (2022) Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand. Sci Total Environ 807:150741. https://doi.org/10.1016/j.scitotenv.2021.150741
Ayugi B, Ngoma H, Babaousmail H, Karim R, Iyakaremye V, Lim Kam Sian KTC, Ongoma V (2021) Evaluation and projection of mean surface temperature using CMIP6 models over East Africa. J Afr Earth Sci 181. https://doi.org/10.1016/j.jafrearsci.2021.104226
Bağçaci SÇ, Yucel I, Duzenli E, Yilmaz MT (2021) Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: a Mediterranean hot spot case, Turkey. Atmos Res 256. https://doi.org/10.1016/j.atmosres.2021.105576
Behera SK, Yamagata T (2001) Subtropical SST dipole events in the southern Indian Ocean. Geophys Res Lett 28:327–330. https://doi.org/10.1029/2000GL011451
Boé J, Terray L, Habets F, Martin E (2007) Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int J Climatol 27:1643–1655. https://doi.org/10.1002/joc.1602
Cannon AJ, Sobie SR, Murdock TQ (2015) Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J Clim 28:6938–6959. https://doi.org/10.1175/JCLI-D-14-00754.1
Chen J, Zhang Q, Huang W, Lu Z, Zhang Z, Chen F (2021) Northwestward shift of the northern boundary of the East Asian summer monsoon during the mid-Holocene caused by orbital forcing and vegetation feedbacks. Quat Sci Rev 268:107136. https://doi.org/10.1016/j.quascirev.2021.107136
Chen W, Jiang Z, Li L (2011) Probabilistic Projections of Climate Change over China under the SRES A1B Scenario Using 28 AOGCMs. J Clim 24:4741–4756. https://doi.org/10.1175/2011JCLI4102.1
Das S (2021) Extreme rainfall estimation at ungauged locations: information that needs to be included in low-lying monsoon climate regions like Bangladesh. J Hydrol 601:126616. https://doi.org/10.1016/j.jhydrol.2021.126616
DeGaetano AT, Castellano CM (2017) Future projections of extreme precipitation intensity-duration-frequency curves for climate adaptation planning in New York State. Clim Serv 5:23–35. https://doi.org/10.1016/j.cliser.2017.03.003
Donnelly C, Greuell W, Andersson J, Gerten D, Pisacane G, Roudier P, Ludwig F (2017) Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Clim Change 143:13–26. https://doi.org/10.1007/s10584-017-1971-7
Dosio A, Jury MW, Almazroui M, Ashfaq M, Diallo I, Engelbrecht FA, Klutse NAB, Lennard C, Pinto I, Sylla MB, Tamoffo AT (2021) Projected future daily characteristics of African precipitation based on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX-CORE) climate models. Clim Dyn 57:3135–3158. https://doi.org/10.1007/s00382-021-05859-w
Edwards AL (1976) The correlation coefficient. In: An introduction to linear regression and correlation. San Francisco, pp 33–46
Elmore KL, Richman MB (2001) Euclidean distance as a similarity metric for principal component analysis. Mon Weather Rev 129:540–549. https://doi.org/10.1175/1520-0493(2001)129%3c0540:EDAASM%3e2.0.CO;2
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci Model Dev 9:1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Eyring V, Cox PM, Flato GM, Gleckler PJ, Abramowitz G, Caldwell P, Collins WD, Gier BK, Hall AD, Hoffman FM, Hurtt GC, Jahn A, Jones CD, Klein SA, Krasting JP, Kwiatkowski L, Lorenz R, Maloney E, Meehl GA, Pendergrass AG, Pincus R, Ruane AC, Russell JL, Sanderson BM, Santer BD, Sherwood SC, Simpson IR, Stouffer RJ, Williamson MS (2019) Taking climate model evaluation to the next level. Nat Clim Chang 9:102–110. https://doi.org/10.1038/s41558-018-0355-y
Field CB, Barros V, Stocker TF, Dahe Q, Jon Dokken D, Ebi KL, Mastrandrea MD, Mach KJ, Plattner GK, Allen SK, Tignor M, Midgley PM (2012) Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change, Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. Cambridge. https://doi.org/10.1017/CBO9781139177245
Gettelman A, Hannay C, Bacmeister JT, Neale RB, Pendergrass AG, Danabasoglu G, Lamarque J-F, Fasullo JT, Bailey DA, Lawrence DM, Mills MJ (2019) High climate sensitivity in the community earth system model version 2 (CESM2). Geophys Res Lett 46:8329–8337. https://doi.org/10.1029/2019GL083978
Gong DY, Ho CH (2002) Shift in the summer rainfall over the Yangtze River valley in the late 1970s. Geophys Res Lett 29:78–1–78–4. https://doi.org/10.1029/2001gl014523
Greve P, Kahil T, Mochizuki J, Schinko T, Satoh Y, Burek P, Fischer G, Tramberend S, Burtscher R, Langan S, Wada Y (2018) Global assessment of water challenges under uncertainty in water scarcity projections. Nat Sustain 1:486–494. https://doi.org/10.1038/s41893-018-0134-9
Gudmundsson L, Bremnes JB, Haugen JE, Engen-Skaugen T (2012) Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations—a comparison of methods. Hydrol Earth Syst Sci 16:3383–3390. https://doi.org/10.5194/hess-16-3383-2012
Haensler A, Saeed F, Jacob D (2013) Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections. Clim Change 121:349–363. https://doi.org/10.1007/s10584-013-0863-8
Hanasaki N, Fujimori S, Yamamoto T, Yoshikawa S, Masaki Y, Hijioka Y, Kainuma M, Kanamori Y, Masui T, Takahashi K, Kanae S (2013) A global water scarcity assessment under Shared Socio-economic Pathways—Part 2: Water availability and scarcity. Hydrol Earth Syst Sci 17:2393–2413. https://doi.org/10.5194/hess-17-2393-2013
Helsel DR, Hirsch RM, Ryberg KR, Archfield SA, Gilroy EJ (2020). Statistical Methods in Water Resources. https://doi.org/10.3133/tm4A3
HRCMWR (2022) The basic information of Huaihe River Basin [WWW Document]. URL http://www.hrc.gov.cn/main/lyjs.jhtml
HRCMWR (2009) Summary of flood control planning in the Huaihe River Basin
Huang R (2015) Research on evolution and countermeasures of drought-floods abrupt alternation events in Huaihe River Basin. China Institute of Water Resources and Hydropower Research
Hussain M, Yusof KW, Mustafa MRU, Mahmood R, Jia S (2018) Evaluation of CMIP5 models for projection of future precipitation change in Bornean tropical rainforests. Theor Appl Climatol 134:423–440. https://doi.org/10.1007/s00704-017-2284-5
IPCC (2021) The Sixth Assessment Report (AR6)
Jach L, Schwitalla T, Branch O, Warrach-Sagi K, Wulfmeyer V (2022) Sensitivity of land–atmosphere coupling strength to changing atmospheric temperature and moisture over Europe. Earth Syst Dyn 13:109–132. https://doi.org/10.5194/esd-13-109-2022
Jach L, Warrach‐Sagi K, Ingwersen J, Kaas E, Wulfmeyer V (2020) Land cover impacts on land‐atmosphere coupling strength in climate simulations with WRF over Europe. J Geophys Res Atmos 125. https://doi.org/10.1029/2019JD031989
Jiang T, Lu Y, Huang J, Wang Y, Su B, Tao H (2020) New scenarios of CMIP6 model (SSP-RCP) and its application in the Huaihe River Basin. Adv Meteorol Sci Technol 10:5
Kafadar K, Bowman AW, Azzalini A (1999) Applied smoothing techniques for data analysis: The Kernel approach with S-PLUS illustrations. J Am Stat Assoc 94:982. https://doi.org/10.2307/2670015
Kamal N, Pachauri S (2019) Mann-Kendall, and Sen’s Slope Estimators for Precipitation Trend Analysis in North-Eastern States of India. Int J Comput Appl 177:7–16. https://doi.org/10.5120/ijca2019919453
Kamworapan S, Thao PTB, Gheewala SH, Pimonsree S, Prueksakorn K (2021) Evaluation of CMIP6 GCMs for simulations of temperature over Thailand and nearby areas in the early 21st century. Heliyon 7. https://doi.org/10.1016/j.heliyon.2021.e08263
Kendall MG (1975) Rank correlation methods. Oxford University Press, New York, NY
Khan N, Shahid S, Ismail T (2019) Spatial distribution of unidirectional trends in temperature and temperature extremes in Pakistan. Theor Appl Climatol 136:899–913. https://doi.org/10.1007/s00704-018-2520-7
Kim J, Ivanov VY, Fatichi S (2016) Climate change and uncertainty assessment over a hydroclimatic transect of Michigan. Stoch Environ Res Risk Assess 30:923–944. https://doi.org/10.1007/s00477-015-1097-2
Koch J, Demirel MC, Stisen S (2018) The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models. Geosci Model Dev 11:1873–1886. https://doi.org/10.5194/gmd-11-1873-2018
Laux P, Dieng D, Portele TC, Wei J, Shang S, Zhang Z, Arnault J, Lorenz C, Kunstmann H (2021a) A high-resolution regional climate model physics ensemble for northern sub-Saharan Africa. Front Earth Sci 9. https://doi.org/10.3389/feart.2021.700249
Laux P, Rötter RP, Webber H, Dieng D, Rahimi J, Wei J, Faye B, Srivastava AK, Bliefernicht J, Adeyeri O, Arnault J, Kunstmann H (2021b) To bias correct or not to bias correct? An agricultural impact modelers’ perspective on regional climate model data. Agric For Meteorol 304–305:108406. https://doi.org/10.1016/j.agrformet.2021.108406
Li X, Liu Y, Wang M, Jiang Y, Dong X (2021) Assessment of the coupled model intercomparison project phase 6 (CMIP6) Model performance in simulating the spatial-temporal variation of aerosol optical depth over Eastern Central China. Atmos Res 261. https://doi.org/10.1016/j.atmosres.2021.105747
Lin H, Wang J, Jiang C (2019) Simulation assessment and future scenario prediction of climate elements in Huai River Basin by CMIP5 models. Pearl River 40:43–50
Liu L, Du L, Liao Y, Li Y, Liang X, Tang J, Zhao Y (2018a) Probability prediction of monthly precipitation over Huaihe River Basin in China in summer based on spatio-temporal statistical downscaling method. Meteorol Mon 44:1464–1470
Liu Z, Zhang X, Fang R (2018b) Multi-scale linkages of winter drought variability to ENSO and the Arctic Oscillation: a case study in Shaanxi, North China. Atmos Res 200:117–125. https://doi.org/10.1016/j.atmosres.2017.10.012
Lu J, Yang H, Griffiths ML, Burls NJ, Xiao G, Yang J, Wang JK, Johnson KR, Xie S, (2021) Asian monsoon evolution linked to Pacific temperature gradients since the Late Miocene. Earth Planet Sci Lett 563:116882. https://doi.org/10.1016/j.epsl.2021.116882
Mann HB (1945) Nonparametric tests against trend. Econometrica 13. https://doi.org/10.2307/1907187
Meaurio M, Zabaleta A, Boithias L, Epelde AM, Sauvage S, Sánchez-Pérez J-M, Srinivasan R, Antiguedad I (2017) Assessing the hydrological response from an ensemble of CMIP5 climate projections in the transition zone of the Atlantic region (Bay of Biscay). J Hydrol 548:46–62. https://doi.org/10.1016/j.jhydrol.2017.02.029
Meehl GA (1995) Global coupled general circulation models [WWW Document]. Bull Am Meteorol Soc. https://doi.org/10.1175/1520-0477-76.6.951
Meehl GA, Boer GJ, Covey C, Latif M, Stouffer RJ (1997) Intercomparison makes for a better climate model. Eos (Washington. DC) 78:445–451. https://doi.org/10.1029/97eo00276
Meng X, Liu L, Miao X, Zhao W, Zhang E, Ji J (2021) Significant influence of Northern Hemisphere high latitude climate on appeared precession rhythm of East Asian summer monsoon after Mid-Brunhes Transition interglacials recorded in the Chinese loess. Catena 197:105002. https://doi.org/10.1016/j.catena.2020.105002
Nashwan MS, Shahid S (2022a) Future precipitation changes in Egypt under the 1.5 and 2.0 °C global warming goals using CMIP6 multimodel ensemble. Atmos Res 265. https://doi.org/10.1016/j.atmosres.2021.105908
Nashwan MS, Shahid S (2022b) Future precipitation changes in Egypt under the 1.5 and 2.0 °C global warming goals using CMIP6 multimodel ensemble. Atmos Res 265. https://doi.org/10.1016/j.atmosres.2021.105908
NOAA (2020) State of the Climate: National Climate Report for Annual 2019, Nature
O’Neill BC, Tebaldi C, van Vuuren DP, Eyring V, Friedlingstein P, Hurtt G, Knutti R, Kriegler E, Lamarque J-F, Lowe J, Meehl GA, Moss R, Riahi K, Sanderson BM (2016) The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci Model Dev 9:3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
Olschewski P, Laux P, Wei J, Böker B, Tian Z, Sun L, Kunstmann H (2023) An ensemble-based assessment of bias adjustment performance, changes in hydrometeorological predictors and compound extreme events in EAS-CORDEX. Weather Clim Extrem 39:100531. https://doi.org/10.1016/j.wace.2022.100531
Ongoma V, Chen H, Gao C (2019) Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa. Theor Appl Climatol 135:893–910. https://doi.org/10.1007/s00704-018-2392-x
Padulano R, Reder A, Rianna G (2019) An ensemble approach for the analysis of extreme rainfall under climate change in Naples (Italy). Hydrol Process 33:2020–2036. https://doi.org/10.1002/hyp.13449
Pan X, Li X, Yang K, He J, Zhang Y, Han X (2014) Comparison of downscaled precipitation data over a mountainous watershed: A case study in the Heihe River Basin. J Hydrometeorol 15:1560–1574. https://doi.org/10.1175/JHM-D-13-0202.1
Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 Climate models’ simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J Clim 20:4356–4376. https://doi.org/10.1175/JCLI4253.1
Phillips N (2020) Climate change made Australia’s devastating fire season 30% more likely. Nature. https://doi.org/10.1038/d41586-020-00627-y
Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192. https://doi.org/10.1007/s00704-009-0134-9
Pierce DW, Cayan DR, Maurer EP, Abatzoglou JT, Hegewisch KC (2015) Improved bias correction techniques for hydrological simulations of climate change. J Hydrometeorol 16:2421–2442. https://doi.org/10.1175/JHM-D-14-0236.1
Pierce DW, Das T, Cayan DR, Maurer EP, Miller NL, Bao Y, Kanamitsu M, Yoshimura K, Snyder MA, Sloan LC, Franco G, Tyree M (2013) Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling. Clim Dyn 40:839–856. https://doi.org/10.1007/s00382-012-1337-9
Pincus R, Batstone CP, Hofmann RJP, Taylor KE, Glecker PJ (2008) Evaluating the present-day simulation of clouds, precipitation, and radiation in climate models. J Geophys Res 113:D14209. https://doi.org/10.1029/2007JD009334
Portele TC, Laux P, Lorenz C, Janner A, Horna N, Fersch B, Iza M, Kunstmann H (2021) Ensemble-tailored pattern analysis of high-resolution dynamically downscaled precipitation fields: example for climate sensitive regions of South America. Front. Earth Sci 9. https://doi.org/10.3389/feart.2021.669427
Pour SH, Shahid S, Chung E-S, Wang X-J (2018) Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh. Atmos Res 213:149–162. https://doi.org/10.1016/j.atmosres.2018.06.006
Preethi B, Mujumdar M, Prabhu A, Kripalani R (2017) Variability and teleconnections of South and East Asian summer monsoons in present and future projections of CMIP5 climate models. Asia-Pacific J Atmos Sci 53:305–325. https://doi.org/10.1007/s13143-017-0034-3
Preethi B, Ramya R, Patwardhan SK, Mujumdar M, Kripalani RH (2019) Variability of Indian summer monsoon droughts in CMIP5 climate models. Clim Dyn 53:1937–1962. https://doi.org/10.1007/s00382-019-04752-x
Qiu Z, Qiao F, Jang CJ, Zhang L, Song Z (2021) Evaluation and projection of global marine heatwaves based on CMIP6 models. Deep Res Part II Top Stud Oceanogr 194:104998. https://doi.org/10.1016/j.dsr2.2021.104998
Rajulapati CR, Abdelmoaty HM, Nerantzaki SD, Papalexiou SM (2022) Changes in the risk of extreme temperatures in megacities worldwide. Clim Risk Manag 36:100433. https://doi.org/10.1016/j.crm.2022.100433
Rajulapati CR, Papalexiou SM, Clark MP, Pomeroy JW (2021) The perils of regridding: examples using a global precipitation dataset. J Appl Meteorol Climatol. https://doi.org/10.1175/JAMC-D-20-0259.1
Roberts NM, Lean HW (2008) Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events. Mon Weather Rev 136:78–97. https://doi.org/10.1175/2007MWR2123.1
Ruan Y, Liu Z, Wang R, Yao Z (2019) Assessing the performance of CMIP5 GCMs for projection of future temperature change over the Lower Mekong Basin. Atmosphere (basel) 10:93. https://doi.org/10.3390/atmos10020093
Sen PK (1968) Estimates of the regression coefficient based on Kendall’s Tau. J Am Stat Assoc 63:1379–1389. https://doi.org/10.1080/01621459.1968.10480934
She D, Xia J, Zhang Y, Du H (2011) The trend analysis and statistical distribution of extreme rainfall events in the Huaihe River Basin in the past 50 years. Acta Geogr Sin 66:1200–1210
Shrestha M, Acharya SC, Shrestha PK (2017) Bias correction of climate models for hydrological modelling—are simple methods still useful? Meteorol Appl 24:531–539. https://doi.org/10.1002/met.1655
Song X, Wang DY, Li F, Zeng XD (2021) Evaluating the performance of CMIP6 Earth system models in simulating global vegetation structure and distribution. Adv Clim Chang Res 12:584–595. https://doi.org/10.1016/j.accre.2021.06.008
Su B, Gemmer M, Jiang T (2008) Spatial and temporal variation of extreme precipitation over the Yangtze River Basin. Quat Int 186:22–31. https://doi.org/10.1016/j.quaint.2007.09.001
Sun P, Qu W, Zhu X, Wu Y, Wang J, Zhang B, Xu M, Dai H (2021) Variation of Hydrothermal Pattern of Huai River Basin from 1959 to 2018. Resour Environ Yangze Basin 30:1366–1377. https://doi.org/10.11870/cjlyzyyhj202106008
Sun Q, Miao C, Duan Q (2015) Projected changes in temperature and precipitation in ten river basins over China in 21st century. Int J Climatol 35:1125–1141. https://doi.org/10.1002/joc.4043
Tatli H, Türkeş M (2011) Empirical orthogonal function analysis of the palmer drought indices. Agric Meteorol 151:981–991. https://doi.org/10.1016/j.agrformet.2011.03.004
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719
Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc A Math Phys Eng Sci 365:2053–2075. https://doi.org/10.1098/rsta.2007.2076
UNFCCC (2015) United nations framework convention on climate change, 2015. Decision 1/cp.21.The Paris agreement
Varela R, Rodríguez-Díaz L, de Castro M, Gómez-Gesteira M (2022) Influence of canary upwelling system on coastal SST warming along the 21st century using CMIP6 GCMs. Glob Planet Change 208:1. https://doi.org/10.1016/j.gloplacha.2021.103692
Vrac M, Drobinski P, Merlo A, Herrmann M, Lavaysse C, Li L, Somot S (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat Hazards Earth Syst Sci 12:2769–2784. https://doi.org/10.5194/nhess-12-2769-2012
Wang L, Chen W, Huang G, Zeng G (2017a) Changes of the transitional climate zone in East Asia: past and future. Clim Dyn 49:1463–1477. https://doi.org/10.1007/s00382-016-3400-4
Wang PX, Wang B, Cheng H, Fasullo J, Guo ZT, Kiefer T, Liu ZY (2017b) The global monsoon across time scales: Mechanisms and outstanding issues. Earth-Sci Rev 174:84–121. https://doi.org/10.1016/j.earscirev.2017.07.006
Wang W, Hu Y, Xu C (2021a) Spatial-temporal variations of heat waves in the Huaihe River Basin from 1961 to 2018. Sci Geogr Sin 41:911–921. https://doi.org/10.13249/j.cnki.sgs.2021.05.019
Wang Z, Han L, Ding R, Li J (2021b) Evaluation of the performance of CMIP5 and CMIP6 models in simulating the South Pacific Quadrupole–ENSO relationship. Atmos Ocean Sci Lett 14:100057. https://doi.org/10.1016/j.aosl.2021.100057
Wang Z, Yao C, Dong J, Yang H (2022) Precipitation characteristic and urban flooding influence of “7·20” extreme rainstorm in Zhengzhou. J Hohai Univ (Nature Sci) 50:17–22. https://doi.org/10.3876/j.issn.1000-1980.2022.03.003
Wei F, Zhang T (2010) Oscillation characteristics of summer precipitation in the Huaihe River valley and relevant climate background. Sci China Earth Sci 53:301–316. https://doi.org/10.1007/s11430-009-0151-7
Wernli H, Paulat M, Hagen M, Frei C (2008) SAL—a novel quality measure for the verification of quantitative precipitation forecasts. Mon Weather Rev 136:4470–4487. https://doi.org/10.1175/2008MWR2415.1
WMO (2021) Water-related hazards dominate disasters in the past 50 years (online), World Meteorological Organization
Wood AW (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res 107:4429. https://doi.org/10.1029/2001JD000659
Xing Z, Yu Z, Wei J, Zhang X, Ma M, Yi P, Ju Q, Wang J, Laux P, Kunstmann H (2022) Lagged influence of ENSO regimes on droughts over the Poyang Lake basin, China. Atmos Res 275:106218. https://doi.org/10.1016/j.atmosres.2022.106218
Xu Y, Sun H, Ji X (2021) Spatial-temporal evolution and driving forces of rainfall erosivity in a climatic transitional zone: A case in Huaihe River Basin, eastern China. Catena 198. https://doi.org/10.1016/j.catena.2020.104993
Xu ZX, Chu Q (2015) Climatological features and trends of extreme precipitation during 1979–2012 in Beijing. China Proc Int Assoc Hydrol Sci 369:97–102. https://doi.org/10.5194/piahs-369-97-2015
Xuan W, Ma C, Kang L, Gu H, Pan S, Xu Y-P (2017) Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province. China Theor Appl Climatol 128:207–222. https://doi.org/10.1007/s00704-015-1704-7
Yan D, Werners SE, Ludwig F, Huang HQ (2015) Hydrological response to climate change: The Pearl River, China under different RCP scenarios. J Hydrol Reg Stud 4:228–245. https://doi.org/10.1016/j.ejrh.2015.06.006
Yang K, He J (2014) China meteorological forcing dataset (1979–2015) [WWW Document]. Natl Tibet Plateau Data Cente. https://doi.org/10.11888/AtmosphericPhysics.tpe.249369.file
Yang K, Zhu L, Chen Y, Zhao L, Qin J, Lu H, Tang W, Han M, Ding B, Fang N (2016) Land surface model calibration through microwave data assimilation for improving soil moisture simulations. J Hydrol 533:266–276. https://doi.org/10.1016/j.jhydrol.2015.12.018
Yang Q, Yu Z, Wei J, Yang C, Gu H, Xiao M, Shang S, Dong N, Gao L, Arnault J, Laux P, Kunstmann H (2021) Performance of the WRF model in simulating intense precipitation events over the Hanjiang River Basin, China—a multi-physics ensemble approach. Atmos Res 248:105206. https://doi.org/10.1016/j.atmosres.2020.105206
Yu Y, Yu P, Wang Y, Tu X, Zhang X, Zhang S, Xu L, Wang X, Liu Z, Wang B (2020) Dependence of annual runoff coefficients on basin size and other properties in a climate transition zone from semi-humid to arid and semi-arid on the Loess Plateau, China. J Hydrol 591:125727. https://doi.org/10.1016/j.jhydrol.2020.125727
Yue S, Pilon P, Phinney B, Cavadias G (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16:1807–1829. https://doi.org/10.1002/hyp.1095
Yue S, Wang C (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manag 18:201–218. https://doi.org/10.1023/B:WARM.0000043140.61082.60
Yue S, Wang CY (2002) Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test. Water Resour Res 38: 1–7. https://doi.org/10.1029/2001WR000861
Yue Y, Yan D, Yue Q, Ji G, Wang Z (2021) Future changes in precipitation and temperature over the Yangtze River Basin in China based on CMIP6 GCMs. Atmos Res 264. https://doi.org/10.1016/j.atmosres.2021.105828
Zhang J, Lun Y, Liu L, Liu Y, Li X, Xu Z (2021) CMIP6 evaluation and projection of climate change over the Tibetan Plateau. J. Beijing Norm. Univ, Sci
Zhang Q, Qiuping W, Siping L, Weifeng L (2011) The model of project site selection of industrial plant based on matter element analysis. In: 2011 International Conference on Electric Technology and Civil Engineering (ICETCE). IEEE, pp 5613–5616. https://doi.org/10.1109/ICETCE.2011.5776467
Zhang R, Sun C, Zhu J, Zhang R, Li W (2020) Increased European heat waves in recent decades in response to shrinking Arctic sea ice and Eurasian snow cover. NPJ Clim Atmos Sci 3:7. https://doi.org/10.1038/s41612-020-0110-8
Zhang W, Pan S, Cao L, Cai X, Zhang K, Xu Y, Xu W (2015) Changes in extreme climate events in eastern China during 1960–2013: A case study of the Huaihe River Basin. Quat Int 380–381:22–34. https://doi.org/10.1016/j.quaint.2014.12.038
Zhang X, Song Y (2014) Optimization of wetland restoration siting and zoning in flood retention areas of river basins in China: A case study in Mengwa, Huaihe River Basin. J Hydrol 519:80–93. https://doi.org/10.1016/j.jhydrol.2014.06.043
Zhang Y, Liu C, You Q, Chen C, Xie W, Ye Z, Li X, He Q (2019) Decrease in light precipitation events in Huai River Eco-economic Corridor, a climate transitional zone in eastern China. Atmos Res 226:240–254. https://doi.org/10.1016/j.atmosres.2019.04.027
Zhao P, Zhang X, Zheng S, Shao Y (2021) Analysis of the impact estimation of heavy rainfall in the upper reaches of huaihe river on the water level of Wangjiaba Flood barrier in 2020. Meteorol Environ Sci 44:9–19. https://doi.org/10.16765/j.cnki.1673-7148.2021.06.002
Zhou J, Wang L, Zhang Y, Guo Y, Li X, Liu W (2015) Exploring the water storage changes in the largest lake over the Tibetan Plateau during 2003–2012 from a basin-wide hydrological modeling. Water Resour Res 51:8060–8086. https://doi.org/10.1002/2014WR015846
Acknowledgements
This research was funded by the Key Project of Water Conservancy Science and Technology in Jiangsu Province (2020005). Xin Li is supported financially by the Chinese Scholarship Council (CSC). Jianhui Wei is supported financially by the German Ministry of Education and Research (BMBF) under the project Mitigating the Risk of compound extreme Flooding events MitRiskFlood, grant number 01LP2005A. Joël Arnault is supported financially by the German Funding Agency under the project COSMIC SENSE II, grant number KU 2090/12-2. We are grateful to the National Meteorological Information Center of China and Qinghai-Tibet Plateau Research Institute of the Chinese Academy of Sciences for providing the meteorological observation data (http://data.cma.gov.cn/) and CMFD datasets (http://data.tpdc.ac.cn/zh-hans/). And we also would like to thank the contribution of scientists worldwide who have contributed to the Coupled Model Intercomparison Project (CMIP).
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All authors contributed to the study's conception and design. Data collection and formal analysis were performed by Xin Li and Guohua Fang. The first draft of the manuscript was written by Xin Li and Jianhui Wei. Joël Arnault, Patrick Laux, Xin Wen, and Harald Kunstamann commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Li, X., Fang, G., Wei, J. et al. Evaluation and projection of precipitation and temperature in a coastal climatic transitional zone in China based on CMIP6 GCMs. Clim Dyn 61, 3911–3933 (2023). https://doi.org/10.1007/s00382-023-06781-z
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DOI: https://doi.org/10.1007/s00382-023-06781-z